Skip to content
English
Return to platform & explore data
  • There are no suggestions because the search field is empty.

What do I need to know about naming Variables?

Variable naming matters because it affects how data appears, groups, and behaves in the Explore Data tool. 

Variable grouping

Variables are grouped by type once data is made available in Explore Data. These groupings determine where Variables appear and how they behave.

There are four Variable types:

  • Topic

  • Quantity

  • Time

  • Location

These types are the same as those shown in the Explore Data tool.

Quantity variables

Measured quantity always appears under Quantity.

  • This is the only Variable classified as a Quantity type

  • It is a numeric (metric) Variable

  • Its Categories represent measurements such as:

    • Number of people

    • Completion rate

    • Percentage

    • Count

Because this Variable defines what is being measured, it must always be numeric.

Topic variables

Topic Variables include all Variables that are not classified as Quantity, Time, or Location.

They are used to describe what the data is about, such as:

  • Program type

  • Service category

  • Demographic group

  • Outcome area

Topic Variables provide contextual meaning but do not require special formatting.

Time variables

Time Variables represent when something occurred. These Variables must use specific naming and category formats in order to support auto-refreshing Insights.

Auto-refreshing Insights automatically update as new time periods are added to the Dataset. To enable this, Time Categories must follow recognised formats.

Supported Time Variables

Common Time Variable names include:

  • Year

  • Year Ending December

  • Financial Year

  • Year Month

  • Year Quarter

  • Year Range

  • Year School Term

  • Year Semester

  • Date

  • Week Starting

  • Week Ending

  • Grant Start / End / Approval (Year, Month, Quarter)

Required Time Category formats

Below are the accepted formats for Time Categories.

Time Variable Accepted format(s) Example(s)
Year YYYY 2026
Year Ending / Year Month YYYY M 2025 December
Financial Year YYYY-YY 2025-26
Year Quarter YYYY M-M

2030 Jan–Mar

2030 Apr–Jun
Year Range

YYYY-YY

YYYY-YYYY

2019-20

2020-2025
Year School Term YYYY T#

2023 T1

2023 T2
Year Semester YYYY S#

2019 S1

2019 S2
Date

DD-M-YYYY

D/M/YYYY

D M YYYY

YYYY-M-D

01-Dec-2021

14/12/2021

18 Feb 2021

2022-12-12
Week Starting / Week Ending

YYYY-MM-DD

DD/MM/YY

2022-10-11

1/6/26

⚠️ If Time Categories do not follow these formats, Insights built on them cannot auto-refresh.

Location variables

Location Variables describe where data applies and are grouped automatically when recognised.

Common Location Variables include:

  • Indigenous Region (IREG)

  • Indigenous Area (IARE)

  • Local Government Area (LGA)

  • Postal Area (POA)

  • State Suburb (SSC)

  • Suburb and Locality (SAL)

  • Statistical Area Level 2 (SA2)

  • Statistical Area Level 3 (SA3)

  • Statistical Area Level 4 (SA4)

  • Greater Capital City Statistical Area (GCCSA)

  • State or Territory

  • Country

  • Primary Health Network

  • Region, District, Division

  • Delivery Postcode

  • Recipient Postcode

Using standard Location Variable names improves consistency and makes geographic filtering and comparison easier in Explore.

Why this matters

Clear, consistent Variable naming:

  • Ensures Variables appear in the correct place in Explore Data

  • Enables features like auto-refreshing Insights

  • Makes data easier to understand for non-technical users

  • Supports reliable aggregation and comparison over time and place

Spending time on Variable naming upfront helps avoid rework later and strengthens confidence in the Insights built from your data.